学习一个东西之前要认清学的是什么
啥是机器学习？
机器学习就算用数据的语言，通过计算来进行回归和预测
包括监督学习，非监督学习，强化学习，深度学习
监督学习：就是用含有标签的数据进行在各种数学模型中进行运算，得到具有比较好正确率的参数，可以在未知的数据中预测标签
那么先用一个小代码来理解一下
用回归模型来看幸福感和城市富裕程度的关系
import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn import linear_model #首先处理幸福的数据 #加载数据 oecd_bli = pd.read_csv("oecd_bli_2015.csv",thousands = ',') oecd_bli = oecd_bli[oecd_bli['Inequality']=='Total'] oecd_bli = oecd_bli.pivot(index = 'Country', columns = 'Indicator',values = 'Value') #接着处理gdp的数据 gdp_per_capita = pd.read_csv('gdp_per_capita.csv',thousands = ',', delimiter = '\t', encoding ='latin1',na_values = 'n/a') gdp_per_capita.rename(columns = {'2015':'GDP per captial'},inplace = True) gdp_per_capita.set_index('Country', inplace = True) gdp_per_capita.head(2) #将两张表融合在一起 full_country_stats = pd.merge(left = oecd_bli, right = gdp_per_capita, left_index = True, right_index = True) full_country_stats.sort_values(by = 'GDP per captial', inplace = True) #划分数据 remove_indices = [0,1,6,8,33,34,35] keep_indices = list(set(range(36)) - set(remove_indices)) sample_data = full_country_stats[["GDP per captial",'Life satisfaction']].iloc[keep_indices] missing_data = full_country_stats[["GDP per captial","Life satisfaction"]].iloc[remove_indices] #画图 sample_data.plot(kind = 'scatter',x= 'GDP per captial',y = 'Life satisfaction', figsize = (5,3)) plt.axis([0,60000,0,10]) position_text = { "Hungary":(5000,1), "Korea":(18000,1.7), "France":(29000,2.4), "Australia":(40000,3.0), "United States":(52000,3.8) } for country, pos_text in position_text.items(): pos_data_x, pos_data_y = sample_data.loc[country] if country == "United States" : country = 'U.S.' else: country plt.annotate(country, xy = (pos_data_x, pos_data_y), xytext = pos_text, arrowprops = dict(facecolor = 'black', width = 0.5, shrink = 0.1, headwidth = 5)) plt.plot(pos_data_x,pos_data_y,'ro')
#选择线性模型 country_stats = sample_data x = np.c_[country_stats['GDP per captial']] y = np.c_[country_stats['Life satisfaction']] # Visualize the data country_stats.plot(kind='scatter', x="GDP per captial", y='Life satisfaction') plt.show() #选择线性模型 lin_reg_model = linear_model.LinearRegression() lin_reg_model.fit(x, y) #Make a prediction for Cyprus X_new = [[22587]] print(lin_reg_model.predict(X_new))
